Global and national influenza-associated hospitalisation rates: Estimates for 40 countries and administrative regions

Background WHO estimates that seasonal influenza epidemics result in three to five million cases of severe illness (hospitalisations) every year. We aimed to improve the understanding of influenza-associated hospitalisation estimates at a national and global level. Methods We performed a systematic literature review of English- and Chinese-language studies published between 1995 and 2020 estimating influenza-associated hospitalisation. We included a total of 127 studies (seven in Chinese) in the meta-analysis and analyzed their data using a logit-logistic regression model to understand the influence of five study factors and produce national and global estimates by age groups. The five study factors assessed were: 1) the method used to calculate the influenza-associated hospitalisation estimates (rate- or time series regression-based), 2) the outcome measure (divided into three envelopes: narrow, medium, or wide), 3) whether every case was laboratory-confirmed or not, 4) whether the estimates were national or sub-national, 5) whether the rates were based on a single year or multiple years. Results The overall pooled influenza-associated hospitalisation rate was 40.5 (95% confidence interval (CI) = 24.3-67.4) per 100 000 persons, with rates varying substantially by age: 224.0 (95% CI = 118.8-420.0) in children aged 0-4 years and 96.8 (95% CI = 57.0-164.3) in the elderly aged >65 years. The overall pooled hospitalisation rates varied by calculation method; for all ages, the rates were significantly higher when they were based on rate-based methods or calculated on a single season and significantly lower when cases were laboratory-confirmed. The national hospitalisation rates (all ages) varied considerably, ranging from 11.7 (95% CI = 3.8-36.3) per 100 000 in New Zealand to 122.1 (95% CI = 41.5-358.4) per 100 000 in India (all age estimates). Conclusions Using the pooled global influenza-associated hospitalisation rate, we estimate that seasonal influenza epidemics result in 3.2 million cases of severe illness (hospitalisations) per annum. More extensive analyses are required to assess the influence of other factors on the estimates (e.g. vaccination and dominant virus (sub)types) and efforts to harmonize the methods should be encouraged. Our study highlights the high rates of influenza-associated hospitalisations in children aged 0-4 years and the elderly aged 65+ years.


Method
We performed a total of three searches in Pubmed and Embase, for studies reporting influenza-associated hospitalisations. Based on the original BIRD search from 2019, we updated our Pubmed search from 11-02-2019 to 12-03-2020. Additionally, we performed a new search in Embase, following the original search terms, but applied to Embase.

Results
Our searches resulted in a total of 4625 records. After removing duplicates, 3906 records remained for title and abstract screening by two reviewers. This resulted in 312 records that were assessed for full text. A total of 135 studies were included for data extraction.

Method
We searched three Chinese-language databases, CNKI, Wanfang and Chongqing VIP for studies reporting flu-ALRI hospitalisations. The search strategy for the Chinese databases was adapted from the English search strategy. The same inclusion and exclusion criteria were applied to the Chinese-language studies.

Results
Our search resulted in 3015 records. After removing duplicates, 2630 records were screened by title and abstract, of which 24 records were screened by full-text. PRISMA flowchart is attached in Figure 1. A total of 7 studies were included in the review. Two teams of researchers independently screened all titles and abstracts for eligibility in the English language search (TvP and JvS) and the Chinese language search (YL and XW). Following this step, fulltexts of the selected studies were independently screened for final inclusion by each researcher. Data from the selected studies were extracted into a standardized format, and were all double checked by a second independent researcher. Discrepancies were resolved by discussion with a third reviewer.
Hospitalization incidence rates were extracted as a rate per 100 000, and were adjusted accordingly if reported in another fashion. As both regional and national studies were considered, we collected the information to determine the geographical location of the study and the population from which the data were sampled, and information on the census population. Statistical methods that were performed to create incidence rates were extracted, and, when reported, corresponding 95% confidence intervals, ranges, standard errors, numerators and denominators as well.

Exclusion criteria
Studies were excluded if: 1. Full text was not available or we had no access, 2. The paper focused on the 2009 pandemic, 3. The study focused on a local hospital only, 4. The hospitalization rates were only reported for a particular subgroup such as pregnant women or those with comorbidities 5. The hospitalizations reported were but focused on ICU and/or ER admissions, or long-term care facilities, 6. The paper was in a language other than English or Chinese, 7. The data were derived from a vaccine effectiveness or efficacy study (e.g. a randomized control trial), 8. There were less than 50 confirmed cases were included in the study in a season/ year, 9. The rates were only reported in ranges (there were no point estimates). For duplicate results, we included the more detailed version of the study.
For the statistical analysis (see below), we excluded estimates or studies that did not fit a number of criteria: • The data did not fit the age groups for which we had sufficient data i.e. children older than 5 years and adults up to the age of 59 years • Studies did not provide 95% confidence intervals or standard errors as we wanted to provide confidence intervals to our estimates

II. Data extractions
A Time stamp Not applicable for manual data extraction B Author + year First author of manuscript and year of publication C Country Country on which study data is reported D National/regional Does the study concern regional or national data? E Region (e.g. city & Province) If regional study, specify region F Seasonal/Annual Does the reported data concern seasonal or annual rates? Note: January -December of the same year is considered annual data, but also data collection running from July one year to June the following year. Hospitalization rates reported, per 100 000. If paper reports rates per 1000 or 10 000, these need to be converted and entered as rates per 100 000.

III. Definitions
Study types: Two types of studies were eligible for inclusion in the literature review: rate-based studies and time series regression-based studies. Rate-based studies were defined as studies that report age group specific hospitalization rates for influenza (generally based on laboratory confirmation or ICD coded diagnosis) (e.g. the multiplier method (4)). Time series regression-based studies were defined as studies that estimate excess hospitalizations using time series regression methods.
Papers with multiple outcomes: If a paper had hospitalisation estimates for multiple outcomes (e.g. 1) Pneumonia and Influenza and 2) Respiratory hospitalisations), we extracted all outcome estimates.
Envelopes: Considering the hospitalization outcome measures varied widely (5) Papers with multiple country estimates: If a paper had multiple country estimates (e.g. three countries), we extracted all estimates.
Season: In temperate countries a season was the winter period (e.g. rate from week 40 to week 20 in the Northern Hemisphere) and in tropical countries it was typically the year (rate over a 12-month period).

IV. Statistical multilevel meta-analysis model
The meta-analysis is done within a multilevel framework with binary outcomes. An advantage of this approach is that it is flexible enough to model the heterogeneous designs of the studies reported in the literature, and to separate the different influences this has on the outcomes. For a more elaborate discussion of these models we refer to the literature (see below) (1) In this model the individual measurement level has two random components (error variance and between measurement variance) (2) In this model the studies are not weighted, as is done in many meta-analysis studies. The reason is that the papers in the literature are so diverse in design and quality that no reasonable weights could be constructed.